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假设一个网络要识别人脸，那么一开始它只能检测到一些边边角角的东西，和人脸根本没有关系；然后可能会检测到一些线条和圆形；慢慢地，可以检测到有人脸的区域，下图是一个简单的示例。这表达了一个什么事实呢？概括来说就是：前面几层都学习到的是通用的特征（general feature）；随着网络层次的加深，后面的网络更偏重于学习任务特定的特征（specific feature）。这非常好理解，我们也都很好接受。那么问题来了：如何得知哪些层能够学习到general feature，哪些层能够学习到specific feature。更进一步：如果应用于迁移学习，如何决定该迁移哪些层、固定哪些层？
一、研究问题


我们可以量化模型的某一层的特征到底是通用的还是具体的吗？
转换是在某一层上突然发生，还是在几层上展开 ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/60088/" title="小型数据库开发"><img class="post-bg" src="https://img1.imgtp.com/2023/07/13/kT2O6m6H.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="小型数据库开发"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/60088/" title="小型数据库开发">小型数据库开发</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-07-13T08:30:47.000Z" title="发表于 2023-07-13 16:30:47">2023-07-13</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%BC%80%E5%8F%91/">开发</a></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-comments"></i><a href="/posts/60088/#post-comment"><span class="valine-comment-count" data-xid="/posts/60088/"><i class="fa-solid fa-spinner fa-spin"></i></span></a><span class="article-meta-label"> 条评论</span></span></div><div class="content">题目自学上层应用访问数据库的方式（如ODBC、ADO、JDBC、MySQLi或者其它），根据您使用的上层语言（不限语言（但要求与自己完成的文件管理数据实验开发语言一致）），不限数据库（除ACCESS，SQLite 以外），选择并学习使用一种合适的访问数据库的方式。基于文件管理数据实现，实现使用关系数据库管理课本中 P70-6 题中 SPJ 数据库。功能包括：开发环境说明：

Python3.6Mysql 8.0.26Navicat Premium 15Pyqt5Qt-designerpymysql

数据库设计有一个SPJ数据库，包括S、P、J及SPJ4个关系模式∶S(SNO,SNAME,STATUS,CITY);P(PNO,PNAME,COLOR,WEIGHT):J(JNO.JINAME,CrITY);SPJ(SNO,PNO,NO,QTY)。供应商表S由供应商代码（SNO）、供应商姓名（SNAME）、供应商状态（STATUS）、供应商所在城市CITY组成。零件表P由零件代码（PNO）、零件名（PNAME）、颜色（COLOR）、重量（WEIGHT组成。工程项目表J由工程项目代码（JNO ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/posts/20451/" title="【剑指offer】梯度消失和梯度爆炸"><img class="post-bg" src="https://img1.imgtp.com/2023/07/12/LeFiuFdv.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【剑指offer】梯度消失和梯度爆炸"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/20451/" title="【剑指offer】梯度消失和梯度爆炸">【剑指offer】梯度消失和梯度爆炸</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-07-13T01:13:58.000Z" title="发表于 2023-07-13 09:13:58">2023-07-13</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%89%91%E6%8C%87offer/">剑指offer</a></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-comments"></i><a href="/posts/20451/#post-comment"><span class="valine-comment-count" data-xid="/posts/20451/"><i class="fa-solid fa-spinner fa-spin"></i></span></a><span class="article-meta-label"> 条评论</span></span></div><div class="content">【剑指offer】系列文章目录BN层详解
反向传播
1*1卷积的作用
常用的数据增强的方法

梯度消失和梯度爆炸梯度消失和梯度爆炸是深度神经网络中常见的问题，这些问题可能导致模型无法训练或者训练过程非常缓慢。【文末配有代码，可以参考代码案例进行理解以下概念】

梯度消失指的是在反向传播过程中，模型的某些层的梯度非常小，甚至接近于0，导致这些层的参数几乎无法更新。这种情况产生的原因有：一是在深层网络中，当网络层数较多时，梯度会在反向传播过程中多次相乘，使得梯度值逐渐变小，最终消失。当梯度消失时，网络的学习效果会变得非常差，甚至无法训练。二是采用了不合适的损失函数，比如sigmoid。当梯度消失发生时，接近于输出层的隐藏层由于其梯度相对正常，所以权值更新时也就相对正常，但是当越靠近输入层时，由于梯度消失现象，会导致靠近输入层的隐藏层权值更新缓慢或者更新停滞。这就导致在训练时，只等价于后面几层的浅层网络的学习。

梯度爆炸指的是在反向传播过程中，模型的某些层的梯度非常大，甚至超过了计算机可以表示的范围，导致这些层的参数发生了非常大的变化。这种情况通常发生在深度神经网络中，当网络层数较多时，梯 ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/8307/" title="【剑指offer】BN层详解"><img class="post-bg" src="https://img1.imgtp.com/2023/07/12/LeFiuFdv.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【剑指offer】BN层详解"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/8307/" title="【剑指offer】BN层详解">【剑指offer】BN层详解</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-07-13T01:10:41.000Z" title="发表于 2023-07-13 09:10:41">2023-07-13</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%89%91%E6%8C%87offer/">剑指offer</a></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-comments"></i><a href="/posts/8307/#post-comment"><span class="valine-comment-count" data-xid="/posts/8307/"><i class="fa-solid fa-spinner fa-spin"></i></span></a><span class="article-meta-label"> 条评论</span></span></div><div class="content">【剑指offer】系列文章目录梯度消失和梯度爆炸
反向传播
1*1卷积的作用
常用的数据增强的方法

BN层的本质原理BN层（Batch Normalization Layer）是深度学习中常用的一种方法，用于加速神经网络的收敛速度，并且可以减小模型对初始参数的依赖性，提高模型的鲁棒性。BN层是在每个mini-batch数据上进行归一化处理，使得神经网络的输入更加平稳，从而有助于提高模型的收敛速度和泛化能力。
BN层的原理是将每个mini-batch数据进行归一化处理，即将每个特征的均值和方差分别减去和除以当前mini-batch数据的均值和方差，以使得每个特征的数值分布在一个相对稳定的范围内。此外，为了保证模型的表达能力，BN层还引入了两个可学习参数gamma和beta，用于调整归一化后的特征值的范围和偏移量。

BN层的优点总结
可以加速神经网络的收敛速度。

减小模型对初始参数的依赖性，提高模型的鲁棒性。

可以防止梯度消失和梯度爆炸的问题，有助于提高模型的稳定性。

可以减少模型过拟合的风险，提高模型的泛化能力。
总之，BN层是一种常用的正则化方法，可以有效地提高神经网络的训练 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/posts/21336/" title="【剑指offer】常用的数据增强的方法"><img class="post-bg" src="https://img1.imgtp.com/2023/07/12/LeFiuFdv.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【剑指offer】常用的数据增强的方法"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/21336/" title="【剑指offer】常用的数据增强的方法">【剑指offer】常用的数据增强的方法</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-07-12T07:23:05.000Z" title="发表于 2023-07-12 15:23:05">2023-07-12</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%89%91%E6%8C%87offer/">剑指offer</a></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-comments"></i><a href="/posts/21336/#post-comment"><span class="valine-comment-count" data-xid="/posts/21336/"><i class="fa-solid fa-spinner fa-spin"></i></span></a><span class="article-meta-label"> 条评论</span></span></div><div class="content">系列文章目录BN层详解
梯度消失和梯度爆炸
反向传播
1*1卷积的作用

常用的数据增强的方法数据增强是指通过对原始数据进行一系列变换来生成更多的训练数据，从而提高模型的泛化能力。常用的数据增强方法包括：

随机裁剪：随机从原图中裁剪一部分区域，然后将其缩放到指定大小。这种方法可以增加模型对不同物体的感知能力，同时也可以减少过拟合。
随机旋转：随机将原图旋转一定角度，以生成不同角度的样本。这种方法可以提高模型对旋转物体的识别能力。
随机缩放：随机将原图缩放到不同尺寸，以生成不同大小的样本。这种方法可以提高模型对不同大小物体的识别能力。
随机翻转：随机将原图水平或垂直翻转，以生成不同方向的样本。这种方法可以提高模型对不同方向物体的识别能力。
随机扰动：在原图中添加噪声或扰动，以生成更多的样本。这种方法可以提高模型对噪声和扰动的鲁棒性。
随机变换颜色：随机改变原图的颜色，如亮度、对比度、饱和度等，以生成更多的样本。这种方法可以提高模型对不同光照条件的识别能力。
模板匹配：在原图中使用不同的模板进行匹配，以生成更多的样本。这种方法可以提高模型对不同物体形态的识别能力。
数据混合：将多个不同的 ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/38654/" title="【剑指offer】1*1卷积的作用"><img class="post-bg" src="https://img1.imgtp.com/2023/07/12/LeFiuFdv.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【剑指offer】1*1卷积的作用"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/38654/" title="【剑指offer】1*1卷积的作用">【剑指offer】1*1卷积的作用</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-07-12T02:37:52.000Z" title="发表于 2023-07-12 10:37:52">2023-07-12</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%89%91%E6%8C%87offer/">剑指offer</a></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-comments"></i><a href="/posts/38654/#post-comment"><span class="valine-comment-count" data-xid="/posts/38654/"><i class="fa-solid fa-spinner fa-spin"></i></span></a><span class="article-meta-label"> 条评论</span></span></div><div class="content">系列文章目录BN层详解
梯度消失和梯度爆炸
反向传播
常用的数据增强的方法

增加网络深度（增加非线性映射次数）首先直接从网络深度来理解，1x1 的卷积核虽小，但也是卷积核，加 1 层卷积，网络深度自然会增加。1x1卷积核，可以在保持feature map尺度不变的（即不损失分辨率）的前提下大幅增加非线性特性（利用后接的非线性激活函数），把网络做的很深。我们知道卷积核越大，它生成的 featuremap 上单个节点的感受野就越大，随着网络深度的增加，越靠后的 featuremap 上的节点感受野也越大。因此特征也越来越抽象。
但有的时候，我们想在不增加感受野的情况下，让网络加深，为的就是引入更多的非线性。而 1x1 卷积核，恰巧可以办到。我们知道，卷积后生成图片的尺寸受卷积核的大小和跨度影响，但如果卷积核是 1x1 ，跨度也是 1，那么生成后的图像大小就并没有变化。
但通常一个卷积过程包括一个激活函数，比如 Sigmoid 和 Relu。所以，在输入不发生尺寸的变化下，却引入了更多的非线性，这将增强神经网络的表达能力
升维或降维无论是升维还是降维，我们都是通过改变卷积核的数量实现的，卷 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/posts/29966/" title="【剑指offer】反向传播"><img class="post-bg" src="https://img1.imgtp.com/2023/07/12/LeFiuFdv.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="【剑指offer】反向传播"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/29966/" title="【剑指offer】反向传播">【剑指offer】反向传播</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-07-12T01:54:00.000Z" title="发表于 2023-07-12 09:54:00">2023-07-12</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%89%91%E6%8C%87offer/">剑指offer</a></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-comments"></i><a href="/posts/29966/#post-comment"><span class="valine-comment-count" data-xid="/posts/29966/"><i class="fa-solid fa-spinner fa-spin"></i></span></a><span class="article-meta-label"> 条评论</span></span></div><div class="content">系列文章目录BN层详解
梯度消失和梯度爆炸
1*1卷积的作用
常用的数据增强的方法

什么是反向传播深度学习中的反向传播（Backpropagation）是一种基于梯度下降法的优化方法，用于计算神经网络中每个参数的梯度值，以便利用梯度下降法或其他优化方法来更新参数，从而最小化损失函数。
反向传播的基本思想是通过链式法则计算整个神经网络中每个参数对损失函数的贡献，以便利用梯度下降法来更新参数。具体来说，反向传播算法从输出层开始，将输出误差反向传播到隐藏层和输入层，计算每个神经元的误差和梯度，并使用梯度下降法来更新参数。反向传播算法的关键在于计算每个神经元的误差和梯度，这可以通过链式法则来实现。
在深度学习中，反向传播是一种非常重要的优化方法，可以用于训练各种类型的神经网络，包括卷积神经网络、循环神经网络和自编码器等。反向传播算法的优化和改进也一直是深度学习研究的热点之一。
反向传播的过程反向传播是深度学习中最基本的优化方法之一，用于计算神经网络中每个参数的梯度值，以便利用梯度下降法或其他优化方法来更新参数，从而最小化损失函数。反向传播的过程可以分为以下几个步骤：
假设我们有一个多层前馈神 ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/posts/59448/" title="第一篇博客"><img class="post-bg" src="https://img1.imgtp.com/2023/07/11/eJS1Opyp.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="第一篇博客"></a></div><div class="recent-post-info"><a class="article-title" href="/posts/59448/" title="第一篇博客">第一篇博客</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2023-07-11T03:16:46.000Z" title="发表于 2023-07-11 11:16:46">2023-07-11</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E9%9A%8F%E7%AC%94/">随笔</a></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-comments"></i><a href="/posts/59448/#post-comment"><span class="valine-comment-count" data-xid="/posts/59448/"><i class="fa-solid fa-spinner fa-spin"></i></span></a><span class="article-meta-label"> 条评论</span></span></div><div class="content">之前就有过做个人博客的想法，但是一直没有付诸实践，一方面是技术栈不是很熟悉，前端和java开发接触不多(菜)；另一个方面是之前一直在CSDN写博客（顺便附上我的CSDN链接：.别拖至春天.)，感觉挺方便，所以也懒得整。拥有一个属于自己的网站应该是每个程序员的理想之一，所以趁着小学期，开始个人博客建设，希望这里可以是属于我的一朵云吧。

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